\section{Impact Statement}

This work contributes to the development of fair and inclusive generative AI systems by introducing FairImagen, a training-free and model-agnostic framework for mitigating demographic bias in text-to-image diffusion models. FairImagen offers a practical and scalable solution for enhancing fairness in image generation without requiring access to model internals or manual prompt design. Its ability to jointly address multiple demographic attributes while preserving visual fidelity makes it particularly well-suited for deployment in real-world applications such as digital media, design, and educational content. By reducing the social harms associated with biased generation and enabling more representative outputs, FairImagen supports the broader goal of responsible and equitable AI deployment.

\section{Ethical Statement}

This research aims to address ethical concerns surrounding bias and representation in text-to-image generative models. Our proposed framework, FairImagen, is designed to reduce the amplification of demographic stereotypes without compromising image quality or user intent. We acknowledge that fairness is a multi-faceted and context-dependent concept, and our method focuses primarily on gender and racial representation, which may not capture the full spectrum of social identities or cultural nuances.

We do not collect any personal or sensitive user data in our experiments. All generated images are produced from synthetic prompts, and demographic groupings are based on commonly used protected attributes in fairness research. While FairImagen mitigates certain biases, we caution against interpreting it as a complete solution to fairness in generative models. Ongoing monitoring, inclusive evaluation, and engagement with affected communities remain essential for ensuring responsible deployment.

Our code and findings will be released to the research community to promote transparency and further development of fair and accountable generative AI.



\section{Limitations}

Despite the strengths of FairImagen as a post-hoc, training-free debiasing framework, several limitations remain. First, the method currently focuses on a limited set of protected attributes—primarily binary gender and a coarse categorization of race. This simplification may overlook more nuanced or intersectional demographic identities, such as non-binary gender expressions or multi-ethnic backgrounds. Second, as FairImagen operates on CLIP-based prompt embeddings, it inherits any intrinsic biases present in the CLIP encoder, which itself is trained on large-scale web data with limited curation. While FairPCA reduces group-dependent variance, it cannot fully disentangle bias that is deeply entangled with semantic meaning. Third, although empirical noise injection and projection dimensionality offer tunable fairness-utility trade-offs, determining the optimal balance often requires empirical tuning and may vary across tasks. Additionally, while the framework performs robustly across a wide range of prompts, its effectiveness may degrade for prompts that are strongly tied to cultural or historical contexts, where bias removal risks semantic distortion. Lastly, our evaluation focuses on a specific benchmark of occupational prompts; broader testing across domains, cultures, and creative settings is needed to fully validate generalizability and uncover edge cases where the method may fail.